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Abstract
Serial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as ‘hit’ and ‘miss’, respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify the data into hit and miss categories in order to achieve data reduction. The quantitative performance established in previous work indicates that CNNs successfully classify serial crystallography data into desired categories [Ke, Brewster, Yu, Ushizima, Yang & Sauter (2018). J. Synchrotron Rad. 25, 655–670], but no qualitative evidence on the internal workings of these networks has been provided. For example, there are no visualization methods that highlight the features contributing to a specific prediction while classifying data in serial crystallography experiments. Therefore, existing deep learning methods, including CNNs classifying serial crystallography data, are like a ‘black box’. To this end, presented here is a qualitative study to unpack the internal workings of CNNs with the aim of visualizing information in the fundamental blocks of a standard network with serial crystallography data. The region(s) or part(s) of an image that mostly contribute to a hit or miss prediction are visualized.
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Authors (6)
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Shahid Nawaz
- Deutsches Elektronen-Synchrotron DESY
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Vahid Rahmani
- Deutsches Elektronen-Synchrotron DESY
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David Pennicard
- Deutsches Elektronen-Synchrotron DESY
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Shabarish Pala Ramakantha Setty
- Deutsches Elektronen-Synchrotron DESY
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Heinz Graafsma
- Mid-Sweden University, Sundsvall
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1107/S1600576723007446
- License
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Journal of Applied Crystallography
no. 56,
pages 1494 - 1504,
ISSN: 1600-5767 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Nawaz S., Rahmani V., Pennicard D., Setty S. P. R., Klaudel B., Graafsma H.: Explainable machine learning for diffraction patterns// Journal of Applied Crystallography -,iss. 5 (2023), s.1494-1504
- DOI:
- Digital Object Identifier (open in new tab) 10.1107/s1600576723007446
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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